Offhand one might think that the hybrid neologism “neuroeconomics” would refer to an interdisciplinary approach to economic theory where neuroscience contributes a novel insight of some sort. The book’s title reinforces this view, giving top billing to the lexical inducers “decisions” and “uncertainty” while the word “brain” straggles at list’s end. In point of fact, economics, properly speaking, has little to do with the new discipline’s thematic content. Author Paul Glimcher, a professor and researcher who heads his own Glimcher Lab at the Center for Neural Science at NYU, begins this highly readable narrative not with a tale from economics but with an anecdote from the annals of automata, Jacques de Vaucanson’s mechanical duck (1738), harking back to an epoch when economics had yet to see the light of day.

The much-ballyhooed duck quickly yields center stage to the star culprit of the show, its compatriot the polymath René Descartes, who a century earlier had fused the distinct realms of geometry and algebra into monistic analytic geometry —a cornerstone of modern mathematics— before tearing asunder body and soul with his idea of physiological dualism. Descartes held that two categories of behaviors existed: simple behaviors in which sensory events triggered deterministic motor responses automatically (or reflexively), and complex behaviors where responses were not entirely predictable (nondeterministic). An explanation of the latter category, Descartes believed, required positing the soul as the requisite mechanism for producing volitional behavior. The former category of fully deterministic behaviors, where no volition or reasoning was necessary, became known as reflexes.

Alas, the reflexological paradigm long remained the only game in town for explanatory purposes and in due course preempted the soul in scientific discourse. Complex behaviors came to be seen as the product of complex chains of reflexes, notwithstanding the enormous difficulties of understanding volition in terms of mindless knee-jerk reactions. Eventually (the book admirably discusses the details I omit of this intellectual journey), the pioneering computational neuroscientist, David Marr, introduced a paradigm-shifting approach: the brain as a goal-seeking (i.e., cybernetic) system.

The second half of the book discusses developments after Marr’s untimely passing. Laboratory experiments of visual-saccadic processing in the primate brain are amply discussed. Of particular interest, the role of economics in the newly proposed theory of neurophysiology is duly presented. According to the book, it boils down to two mathematical approaches to decision making: Bayesian decision-theoretic modeling, widely known as decision analysis, and game theory.

Now, decision analysis and game theory are excellent tools for analyzing economic problems, granted. But they are neither limited to economics nor did they arise within the confines of that field. They are actually the product of decision-theoretic mathematics that find application in economics but also in behavioral ecology, artificial intelligence, the theory of evolution, strategic management, military science, public policy analysis, contemporary philosophy, communication network engineering, politics, law, love, poker tournaments and what have you. On the other hand, standard economic concepts such as the law of demand, price elasticities, marginal revenues and costs, inflation, business cycles, market failure and the principal-agent problem, to mention a mere few, are conspicuously absent. Why then should economics get the credit?

I would have gone along with Marr’s vision and christened the thing “neurocybernetics,” which is, after all, the essence of the new paradigm. It also dovetails with Vaucanson’s duck, which clearly owes nothing to economics. Aside from that, the book offers a great read in the history and current developments of neuroscience. If you think neurons are fairly interesting, you will most likely enjoy this book.

Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence

by Matt Carter (2007, Edinburgh UP)

If you are looking for a comprehensive interdisciplinary overview of the core concepts of cognitive science lucidly presented in a couple of hundred pages, seek no more. Matt Carter accomplishes just that by examining the relation between minds and computers, a goal which takes him beyond the bounds of conventional artificial intelligence. In addition to classical AI, Carter ...

Simulating Minds: The Philosophy, Psychology, and Neuroscience of Mindreading

by Alvin I. Goldman (2006, Oxford UP)

In Simulating Minds, his ninth and latest book, Alvin Goldman provides a comprehensive survey of the principal theories devised to explain the mind’s ability to ascribe mental states to other minds as well as to itself. Minds —human and to all appearances those of other intelligent fellow creatures— possess the capability not only of having mental states (things such as n...